Fail Loudly: An Auditable Runtime for Agentic Data Analysis
Large language models (LLMs) have enabled data-science agents to automate multi-step analyses over heterogeneous files. However, incorrect choices regarding data sources, scope, or statistical definitions often lead to silent errors: computations execute successfully but produce plausible yet incorrect outputs that fai...
Han-Xu Yan, Lang-Xuan Deng, Zheng-Le Wang et al.
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